Information constraints in multiple agent problems with i.i.d. states

International audienceIn this chapter we describe several recent results on the problem of coordination among agents when they have partial information about a state which affects their utility, payoff, or reward function. The state is not controlled and rather evolves according to an independent and identically distributed (i.i.d.) random process. This random process might represent various phenomena. In control, it may represent a perturbation or model uncertainty. In the context of smart grids, it may represent a forecasting noise [1]. In wireless communications, it may represent the state of the global communication channel. The approach used is to exploit Shannon theory to characterize the achievable long-term utility region. Two scenarios are described. In the first scenario, the number of agents is arbitrary and the agents have causal knowledge about the state. In the second scenario, there are only two agents and the agents have some knowledge about the future of the state, making its knowledge non-causal